Work has progressed in development and characterization of new methods of analysis for statistically dependent outcomes. We have modified our within-cluster resampling to develop a subject-specific method based on selecting two at a time (one case and one control) from each cluster, in an approach we call within-cluster paired resampling (WCPR). Statistically-dependent data arise in many research contexts. In toxicology, pup-specific outcomes may be more similar for pups from the same litter than for pups from different litters, causing the litter effects problem. When assessing effects of exposures on risk of miscarriage, pregnancy outcomes tend to be more similar among pregnancies from a given woman, because of variability in baseline risk or response from woman to woman. We had developed a method based on sampling one outcome per cluster, carrying out a classical analysis on these now-independent outcomes, and then repeating the resampling many times, ultimately pooling the parameter estimates. Within Cluster Resampling (WCR) performs well in simulations, is analytically tractable, and obviates untestable assumptions about the underlying covariance structure. We have now modified the method to permit a subject-specific analysis. The idea with WCPR is to sample an affected and an unaffected individual from each cluster and compare them with regard to covariate differences by fitting a paired-data logistic regression model. (This approach is only useful in contexts where there is diversity within clusters in the exposures of interest. When such diversity exists, it is often of interest to make within-cluster comparisons, to address, for example, how an individuals risk would change with cessation of smoking.) The paired resampling is repeated numerous times and the separate estimates pooled, as in WCR. In simulations, the method compares very favorably with conditional logistic regression (CLR) in scenarios where the assumptions required by CLR are met. But when the response to an exposure varies across clusters, the dependency structure becomes complex and the assumptions required for CLR are violated, whereas WCPR remains valid. Thus, WCPR is more broadly applicable than the standard method. WCPR may prove most useful in genetic studies where affected and unaffected siblings are to be compared with respect to an allele that may be in linkage disequilibrium with a disease gene. Response is heterogeneous in such a scenario: some parents will carry the disease gene linked to the marker allele; others may carry the marker but not the disease gene; and still others may carry the disease gene, but linked to the wrong allele. The proposed method remains valid despite this heterogeneity across families. - pooled exposure assessment, dependent data, litter effect, within-cluster resampling, binary outcomes